ArticleNature communications2023
Differentially private knowledge transfer for federated learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- scKSFD: federated distillation model with knowledge sharing for cell type classification of clinical transcriptome data.BMC bioinformatics · 2026Article
- Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.Environmental science and ecotechnology · 2025Article
- Incentivizing inclusive contributions in model sharing markets.Nature communications · 2025Article
- Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025Article
- Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.Radiology. Artificial intelligence · 2025Review
- Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.Journal of the American Chemical Society · 2024Article
- Selective knowledge sharing for privacy-preserving federated distillation without a good teacher.Nature communications · 2024Article
- Differentially private knowledge transfer for federated learning.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.